A computer vision system was used to evaluate external physical damage, mold contamination, and flouryto-vitreous endosperm ratio in corn and mold contamination in soybeans. For each of these quality factors, optimal conditions for acquiring video images and processing algorithms were developed. White light in front-lighting mode with a black background for the sample was suitable for all defects except for mold contamination which required use of red light (610 nm). The image processing algorithms were suitable for defect detection in samples both individually and in groups. The average success rates for detecting broken, chipped, starch-cracked and moldy corn kernels were 100%, 83%, 88% and 84%, respectively. The success rate for detecting moldy soybeans was 80%. Physical Damage Initial damage to corn kernel pericarp is caused by combine harvesting (Roberts, 1972). Subsequent handling during various conditioning and processing steps significantly contributes to the physical damage
A N image processing algorithm was developed for detecting stress cracks in corn kernels using a commercial vision system. White light in back-lighting mode with black-coated background having a small aperture for the light provided the best viewing conditions. The kernel images, when processed using the algorithm developed, produced white streaks corresponding to the stress cracks. Double stress cracks were the easiest to detect. Careful positioning of the kernel over the lighting aperture was necessary for satisfactory detection of single and multiple stress cracks.
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